scholarly journals Optimization Method for Transit Signal Priority considering Multirequest under Connected Vehicle Environment

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Song Xianmin ◽  
Yuan Mili ◽  
Liang Di ◽  
Ma Lin
Keyword(s):  
Travel Time ◽  
Optimization Method ◽  
Connected Vehicle ◽  
Transit Signal Priority ◽  
Travel Time Prediction ◽  
Arrival Times ◽  
Vehicle Delay ◽  
Priority Needs ◽  
Bus Travel Time ◽  
Real Time Information

Aiming at reducing per person delay, this paper presents an optimization method for Transit Signal Priority (TSP) considering multirequest under connected vehicle environment, which is based on the travel time prediction model. Conventional arrival time of transit depended on the detection information and the front road state, which restricted the effect of priority seriously. According to the bidirectional and real-time information transmission under connected vehicle environment, this paper establishes a more accurate forecasting model of bus travel time. Based on minimizing the total person delay at the intersection, the decision mechanism of multirequest is devised to meet the priority needs of buses with different arrival times. And the green time compensation algorithm is developed after considering the arrival information of the buses in the next cycle of compensational phase. Finally, the paper combines the COM interface of VISSIM and Matlab to achieve the proposed method under connected vehicle environment. Four control methods were tested when the VCR was 0.5, 0.7, and 0.9. The results illustrated that the proposed method reduced per person delay by 18.57%, 11.88%, and 18.96% and decreased the private vehicle delay by 3.73%, 7.62%, and 13.10%, respectively.

Field Evaluation of Connected Vehicle-Based Transit Signal Priority Control under Two Different Signal Plans

2020 ◽  
Vol 2674 (7) ◽  
pp. 172-180
Author(s):  
Qinzheng Wang ◽  
Xianfeng (Terry) Yang ◽  
Blaine D. Leonard ◽  
Jamie Mackey
Keyword(s):  
Travel Time ◽  
Salt Lake ◽  
Salt Lake City ◽  
Field Evaluation ◽  
Connected Vehicle ◽  
Transit Signal Priority ◽  
Running Time ◽  
Lake City ◽  
Transit Operation ◽  
Bus Travel Time

In 2017, a connected vehicle (CV) corridor utilizing dedicated short-range communication (DSRC) technology was built along Redwood Road, Salt Lake City, Utah. One main goal of this CV corridor is to implement transit signal priority (TSP) when the bus is behind its published schedule by a certain threshold. With the data generated by the transit vehicles, transmitted through the DSRC system, logged by traffic signal controller, and coupled with the Utah Transit Authority (UTA) data from transit operation system, some performance data of the TSP can be analyzed including TSP requested, TSP served, bus reliability, bus travel time, and bus running time. For providing better signal coordination to buses, the signal plan for this CV corridor underwent retiming in October 2018. This research aims to compare the TSP performance before and after the signal retiming. The field data of August, September, November, and December in 2018 were selected to perform this evaluation. Results show that the TSP served rate after signal retiming is 35.29%, which is higher than that of 33.12% before signal retiming. In addition, compared with the signal plan before October, bus reliability northbound and southbound on the CV corridor was improved by 2.4% and 1.47%, respectively; bus travel time and bus running time were reduced as well.

scholarly journals Dynamic Bus Travel Time Prediction Models on Road with Multiple Bus Routes

2015 ◽  
Vol 2015 ◽  
pp. 1-9 ◽  
Author(s):  
Cong Bai ◽  
Zhong-Ren Peng ◽  
Qing-Chang Lu ◽  
Jian Sun
Keyword(s):  
Travel Time ◽  
Dynamic Model ◽  
Kalman Filtering ◽  
Prediction Models ◽  
Support Vector ◽  
Travel Times ◽  
Travel Time Prediction ◽  
Real World Data ◽  
Time Prediction ◽  
Bus Travel Time

Accurate and real-time travel time information for buses can help passengers better plan their trips and minimize waiting times. A dynamic travel time prediction model for buses addressing the cases on road with multiple bus routes is proposed in this paper, based on support vector machines (SVMs) and Kalman filtering-based algorithm. In the proposed model, the well-trained SVM model predicts the baseline bus travel times from the historical bus trip data; the Kalman filtering-based dynamic algorithm can adjust bus travel times with the latest bus operation information and the estimated baseline travel times. The performance of the proposed dynamic model is validated with the real-world data on road with multiple bus routes in Shenzhen, China. The results show that the proposed dynamic model is feasible and applicable for bus travel time prediction and has the best prediction performance among all the five models proposed in the study in terms of prediction accuracy on road with multiple bus routes.

Real time bus travel time prediction using k-NN classifier

2017 ◽  
Vol 11 (7) ◽  
pp. 362-372 ◽  
Author(s):  
B. Anil Kumar ◽  
R. Jairam ◽  
Shriniwas S. Arkatkar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Real Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time

scholarly journals Multi-output bus travel time prediction with convolutional LSTM neural network

2019 ◽  
Vol 120 ◽  
pp. 426-435 ◽  
Author(s):  
Niklas Christoffer Petersen ◽  
Filipe Rodrigues ◽  
Francisco Camara Pereira
Keyword(s):  
Neural Network ◽  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Bus Travel Time ◽  
Convolutional Lstm

Bus travel time prediction based on deep belief network with back-propagation

2019 ◽  
Vol 32 (14) ◽  
pp. 10435-10449 ◽  
Author(s):  
Chao Chen ◽  
Hui Wang ◽  
Fang Yuan ◽  
Huizhong Jia ◽  
Baozhen Yao
Keyword(s):  
Travel Time ◽  
Back Propagation ◽  
Deep Belief Network ◽  
Travel Time Prediction ◽  
Belief Network ◽  
Time Prediction ◽  
Bus Travel Time

Bus travel time prediction: a log-normal auto-regressive (AR) modelling approach

2020 ◽  
Vol 16 (3) ◽  
pp. 807-839 ◽  
Author(s):  
B. Dhivya Bharathi ◽  
B. Anil Kumar ◽  
Avinash Achar ◽  
Lelitha Vanajakshi
Keyword(s):  
Travel Time ◽  
Travel Time Prediction ◽  
Time Prediction ◽  
Auto Regressive ◽  
Log Normal ◽  
Bus Travel Time ◽  
Modelling Approach
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